ChatGPT for Developers: Mastery — Building with the OpenAI API

Build reliable OpenAI API integrations with the Responses API, structured outputs, tool calling and the Agents SDK. Compare hosted File Search with custom RAG and ship a tested agent with code-enforced guardrails, evals, cost and rate-limit controls

What this course covers

The course runs across 12 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Responses API: the current integration boundary — OpenAI SDK client.responses.create, output items/output_text, incomplete/refusal/errors and streaming; no legacy tool-message schema.
Structured outputs and validation — Supported JSON Schema vs JSON mode; handle refusal/truncation, validate business rules; schema validity never authorizes action.
Function calling: an application-owned loop — function_call items, validated allowlisted functions, function_call_output with original call_id/string output; preserve response context and bound loop.
Agents SDK: tools, handoffs, guardrails and tracing — OpenAI Agents SDK orchestration distinct from ChatGPT agent/Codex; application-owned authorization before side effects.
File Search and source-grounded answers — Responses hosted file_search/vector stores, citations/permissions/missing evidence; no manual embeddings required for hosted search.
Embeddings and a custom RAG pipeline — Custom embeddings/chunks/metadata/tenant filters/retrieval/grounded generation; compare hosted search; embeddings alone not RAG.
Guardrails in code, not prompts — Allowlists/schema/business checks, approvals, data boundaries and injection defenses before side effects; reject unknown/malformed tools.
Task-specific evals and regression tests — Reproducible positive/negative datasets, explicit scores/analysis before rollout; check current platform tooling, not a required long-lived Evals product.
Cost and prompt caching — Measure token/call cost, stable prefixes where supported, trim retrieval, validated model choice; caching conditional, not a rate-limit bypass.
Rate limits, retries and observability — Bound tokens/turns/time; transient backoff/jitter, idempotency, metadata logs without secrets; stop on exhausted budgets.
Capstone: a production-minded agent — Fake Responses loop/tool registry, grounded-policy plan, refund guards/human approvals/errors; no credentials/paid calls needed for grading.
Ship with evaluation and an operational handoff — Verified code, deterministic tests, grounded retrieval, cost/retry budgets and spec; optional API practice uses approved secrets outside source.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

Software developers and technical builders. The material is pitched at expert level, and takes roughly 180 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Tool: ChatGPT track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

All ChatGPT training courses →
ChatGPT: Mastery — Leading Work with ChatGPT — Lead reliable work with ChatGPT in any role using the 4Ds: delegation, description, discernment and diligence. Redesign a workflow, package expertise, supervise Work when it acts, run team tasks within your company's governance, and measure impact and spend without a technical background; code and integrations belong in ChatGPT for Developers.
ChatGPT: Advanced — Power Workflows & Automation — The step between everyday fluency and leading work with ChatGPT, without writing code. Learners design multi-step workflows, automate recurring work with Work, plugins, Scheduled tasks, Custom GPTs and Sites, keep long-running tasks on track, and set personal systems that compound over weeks. They finish as the person on the team others come to for ChatGPT technique.
ChatGPT for Developers: Advanced — Agentic Engineering with Codex — Agentic engineering with Codex for experienced developers: plan long tasks, run parallel threads in worktrees and with subagents, back instructions with hooks and guardrails, share skills and plugins across a team, review pull requests with Codex on GitHub, run Codex headless with codex exec and the official GitHub Action, lock down sandbox and admin policy, and control cost and speed by measuring real runs.
ChatGPT: Core — Custom GPTs, Data Analysis & Reasoning — A hands-on working level for experienced ChatGPT users in any role. Build Custom GPTs and Projects, analyze uploaded data, compare reasoning approaches, bring your apps into Work through plugins and @mentions, and run Work tasks with a plan and approvals. No technical background needed; for code and integrations take ChatGPT for Developers.
What's New in ChatGPT for Developers — A short refresher on the latest ChatGPT, Codex and OpenAI API updates for developers
ChatGPT for Developers Pathway — ChatGPT for developers, from coding in chat (Essentials), through Codex in the ChatGPT app and MCP (Core) and agentic engineering with Codex (Advanced), to production-minded OpenAI Responses API agents (Mastery). Also includes 4 optional playbooks (Testing, Code Review, Refactoring & Migration, Docs & Onboarding) to take any time after Essentials; they don't count toward the certificate.

Frequently asked questions

How long does this course take?

It runs roughly 180 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code TUL-GPT-DEV-301.